zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy-Reference-Cited by-同舟云学术

zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy

Published:2021-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
language:
Short-container-title:

Author:

Liu Tianyi¹,Xie Xiang²,Zhang Yupeng³

Affiliation:

1. Texas A&M University & Shanghai Key Laboratory of Privacy-Preserving Computation, College Station, TX, USA

2. Shanghai Key Laboratory of Privacy-Preserving Computation, Shanghai, China

3. Texas A&M University, College Station, TX, USA

Funder

DARPA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3460120.3485379

Reference52 articles.

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2. [n.d.]. Implementation of vCNN: Verifiable Convolutional Neural Network based on zk-SNARKs. https://github.com/snp-labs/VCNN. [n.d.]. Implementation of vCNN: Verifiable Convolutional Neural Network based on zk-SNARKs. https://github.com/snp-labs/VCNN.

3. [n.d.]. Implementation of ZEN: Efficient Zero-Knowledge Proof for Neural Networks. https://github.com/UCSB-TDS/ZEN. [n.d.]. Implementation of ZEN: Efficient Zero-Knowledge Proof for Neural Networks. https://github.com/UCSB-TDS/ZEN.

4. [n.d.]. mcl. https://github.com/herumi/mcl/. [n.d.]. mcl. https://github.com/herumi/mcl/.

5. 2021. TensorFlow. https://www.tensorflow.org/. 2021. TensorFlow. https://www.tensorflow.org/.

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